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<div class="title">hv_go.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2012 Aitor Aldoma, Federico Tombari</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
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<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
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<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
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<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160; </div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor">#ifndef PCL_RECOGNITION_IMPL_HV_GO_HPP_</span></div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#define PCL_RECOGNITION_IMPL_HV_GO_HPP_</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160; </div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#include &lt;pcl/recognition/hv/hv_go.h&gt;</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#include &lt;numeric&gt;</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="common_2time_8h.html">pcl/common/time.h</a>&gt;</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="common_2include_2pcl_2point__types_8h.html">pcl/point_types.h</a>&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160; </div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T, <span class="keyword">typename</span> NormalT&gt;</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">void</span> extractEuclideanClustersSmooth(<span class="keyword">const</span> <span class="keyword">typename</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointT&gt;</a> &amp;cloud, <span class="keyword">const</span> <span class="keyword">typename</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;NormalT&gt;</a> &amp;normals, <span class="keywordtype">float</span> tolerance,</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;    <span class="keyword">const</span> <span class="keyword">typename</span> pcl::search::Search&lt;PointT&gt;::Ptr &amp;tree, std::vector&lt;pcl::PointIndices&gt; &amp;clusters, <span class="keywordtype">double</span> eps_angle, <span class="keywordtype">float</span> curvature_threshold,</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> min_pts_per_cluster, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_pts_per_cluster = (std::numeric_limits&lt;int&gt;::max) ())</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;{</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <span class="keywordflow">if</span> (tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">getInputCloud</a> ()-&gt;points.size () != cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  {</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    PCL_ERROR(<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset\n&quot;</span>);</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  }</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;  <span class="keywordflow">if</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size () != normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ())</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    PCL_ERROR(<span class="stringliteral">&quot;[pcl::extractEuclideanClusters] Number of points in the input point cloud different than normals!\n&quot;</span>);</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;  }</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160; </div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;  <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  std::vector&lt;bool&gt; processed (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160; </div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;  std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  <span class="comment">// Process all points in the indices vector</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  <span class="keywordtype">int</span> size = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (cloud.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; size; ++i)</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  {</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    <span class="keywordflow">if</span> (processed[i])</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    std::vector&lt;unsigned int&gt; seed_queue;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;    <span class="keywordtype">int</span> sq_idx = 0;</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    seed_queue.push_back (i);</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160; </div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    processed[i] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160; </div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="keywordflow">while</span> (sq_idx &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (seed_queue.size ()))</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    {</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160; </div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;      <span class="keywordflow">if</span> (normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].curvature &gt; curvature_threshold)</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      {</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;      }</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160; </div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;      <span class="comment">// Search for sq_idx</span></div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;      <span class="keywordflow">if</span> (!tree-&gt;<a class="code" href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">radiusSearch</a> (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;      {</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        sq_idx++;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;      }</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160; </div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 1; j &lt; nn_indices.size (); ++j) <span class="comment">// nn_indices[0] should be sq_idx</span></div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      {</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        <span class="keywordflow">if</span> (processed[nn_indices[j]]) <span class="comment">// Has this point been processed before ?</span></div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160; </div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <span class="keywordflow">if</span> (normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].curvature &gt; curvature_threshold)</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        }</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160; </div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        <span class="comment">//processed[nn_indices[j]] = true;</span></div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="comment">// [-1;1]</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160; </div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        <span class="keywordtype">double</span> dot_p = normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[0] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[0]</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;            + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[1] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[1]</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;            + normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[seed_queue[sq_idx]].normal[2] * normals.<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[nn_indices[j]].normal[2];</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160; </div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        <span class="keywordflow">if</span> (fabs (acos (dot_p)) &lt; eps_angle)</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        {</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;          processed[nn_indices[j]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;          seed_queue.push_back (nn_indices[j]);</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        }</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;      }</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;      sq_idx++;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    }</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160; </div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <span class="comment">// If this queue is satisfactory, add to the clusters</span></div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <span class="keywordflow">if</span> (seed_queue.size () &gt;= min_pts_per_cluster &amp;&amp; seed_queue.size () &lt;= max_pts_per_cluster)</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    {</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> r;</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;      r.indices.resize (seed_queue.size ());</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; seed_queue.size (); ++j)</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        r.indices[j] = seed_queue[j];</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      std::sort (r.indices.begin (), r.indices.end ());</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;      r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160; </div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;      r.header = cloud.<a class="code" href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">header</a>;</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;      clusters.push_back (r); <span class="comment">// We could avoid a copy by working directly in the vector</span></div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  }</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;}</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;mets::gol_type <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::evaluateSolution</a>(<span class="keyword">const</span> std::vector&lt;bool&gt; &amp; active, <span class="keywordtype">int</span> changed)</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;{</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="keywordtype">float</span> sign = 1.f;</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  <span class="comment">//update explained_by_RM</span></div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  <span class="keywordflow">if</span> (active[changed])</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  {</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    <span class="comment">//it has been activated</span></div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    updateExplainedVector (recognition_models_[changed]-&gt;explained_, recognition_models_[changed]-&gt;explained_distances_, explained_by_RM_,</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        explained_by_RM_distance_weighted, 1.f);</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    updateUnexplainedVector (recognition_models_[changed]-&gt;unexplained_in_neighborhood, recognition_models_[changed]-&gt;unexplained_in_neighborhood_weights,</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        unexplained_by_RM_neighboorhods, recognition_models_[changed]-&gt;explained_, explained_by_RM_, 1.f);</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    updateCMDuplicity(recognition_models_[changed]-&gt;complete_cloud_occupancy_indices_, complete_cloud_occupancy_by_RM_, 1.f);</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;  } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;  {</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="comment">//it has been deactivated</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    updateExplainedVector (recognition_models_[changed]-&gt;explained_, recognition_models_[changed]-&gt;explained_distances_, explained_by_RM_,</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;        explained_by_RM_distance_weighted, -1.f);</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    updateUnexplainedVector (recognition_models_[changed]-&gt;unexplained_in_neighborhood, recognition_models_[changed]-&gt;unexplained_in_neighborhood_weights,</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        unexplained_by_RM_neighboorhods, recognition_models_[changed]-&gt;explained_, explained_by_RM_, -1.f);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    updateCMDuplicity(recognition_models_[changed]-&gt;complete_cloud_occupancy_indices_, complete_cloud_occupancy_by_RM_, -1.f);</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    sign = -1.f;</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;  }</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;  <span class="keywordtype">int</span> duplicity = getDuplicity ();</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  <span class="keywordtype">float</span> good_info = getExplainedValue ();</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  <span class="keywordtype">float</span> unexplained_info = getPreviousUnexplainedValue ();</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;  <span class="keywordtype">float</span> bad_info = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (getPreviousBadInfo ())</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;      + (recognition_models_[changed]-&gt;outliers_weight_ * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (recognition_models_[changed]-&gt;bad_information_)) * sign;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  setPreviousBadInfo (bad_info);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  <span class="keywordtype">int</span> n_active_hyp = 0;</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i=0; i &lt; active.size(); i++) {</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;    <span class="keywordflow">if</span>(active[i])</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      n_active_hyp++;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  }</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160; </div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  <span class="keywordtype">float</span> duplicity_cm = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (getDuplicityCM ()) * w_occupied_multiple_cm_;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  <span class="keywordflow">return</span> <span class="keyword">static_cast&lt;</span>mets::gol_type<span class="keyword">&gt;</span> ((good_info - bad_info - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (duplicity) - unexplained_info - duplicity_cm - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (n_active_hyp)) * -1.f); <span class="comment">//return the dual to our max problem</span></div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;}</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; </div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::initialize</a>()</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;{</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  <span class="comment">//clear stuff</span></div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;  recognition_models_.clear ();</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  unexplained_by_RM_neighboorhods.clear ();</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  explained_by_RM_distance_weighted.clear ();</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;  explained_by_RM_.clear ();</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  mask_.clear ();</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;  indices_.clear (),</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  complete_cloud_occupancy_by_RM_.clear ();</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160; </div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  <span class="comment">// initialize mask to false</span></div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;  mask_.resize (complete_models_.size ());</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; complete_models_.size (); i++)</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    mask_[i] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160; </div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  indices_.resize (complete_models_.size ());</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  NormalEstimator_ n3d;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;  scene_normals_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::Normal&gt;</a> ());</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;  <span class="keyword">typename</span> pcl::search::KdTree&lt;SceneT&gt;::Ptr normals_tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;SceneT&gt;</a>);</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;  normals_tree-&gt;setInputCloud (scene_cloud_downsampled_);</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160; </div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;  n3d.setRadiusSearch (radius_normals_);</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;  n3d.setSearchMethod (normals_tree);</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  n3d.setInputCloud (scene_cloud_downsampled_);</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  n3d.compute (*scene_normals_);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  <span class="comment">//check nans...</span></div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  <span class="keywordtype">int</span> j = 0;</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; scene_normals_-&gt;points.size (); ++i)</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  {</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keywordflow">if</span> (!pcl_isfinite (scene_normals_-&gt;points[i].normal_x) || !pcl_isfinite (scene_normals_-&gt;points[i].normal_y)</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        || !pcl_isfinite (scene_normals_-&gt;points[i].normal_z))</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160; </div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    scene_normals_-&gt;points[j] = scene_normals_-&gt;points[i];</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    scene_cloud_downsampled_-&gt;points[j] = scene_cloud_downsampled_-&gt;points[i];</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160; </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    j++;</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  }</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; </div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;  scene_normals_-&gt;points.resize (j);</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;  scene_normals_-&gt;width = j;</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;  scene_normals_-&gt;height = 1;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;  scene_cloud_downsampled_-&gt;points.resize (j);</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;  scene_cloud_downsampled_-&gt;width = j;</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;  scene_cloud_downsampled_-&gt;height = 1;</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160; </div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;  explained_by_RM_.resize (scene_cloud_downsampled_-&gt;points.size (), 0);</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;  explained_by_RM_distance_weighted.resize (scene_cloud_downsampled_-&gt;points.size (), 0.f);</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  unexplained_by_RM_neighboorhods.resize (scene_cloud_downsampled_-&gt;points.size (), 0.f);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;  <span class="comment">//compute segmentation of the scene if detect_clutter_</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;  <span class="keywordflow">if</span> (detect_clutter_)</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;  {</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    <span class="comment">//initialize kdtree for search</span></div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    scene_downsampled_tree_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;SceneT&gt;</a>);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    scene_downsampled_tree_-&gt;setInputCloud (scene_cloud_downsampled_);</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160; </div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    std::vector&lt;pcl::PointIndices&gt; clusters;</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    <span class="keywordtype">double</span> eps_angle_threshold = 0.2;</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    <span class="keywordtype">int</span> min_points = 20;</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    <span class="keywordtype">float</span> curvature_threshold = 0.045f;</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    extractEuclideanClustersSmooth&lt;SceneT, pcl::Normal&gt; (*scene_cloud_downsampled_, *scene_normals_, inliers_threshold_ * 2.f, scene_downsampled_tree_,</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        clusters, eps_angle_threshold, curvature_threshold, min_points);</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160; </div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;    clusters_cloud_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointXYZI&gt;</a>);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    clusters_cloud_-&gt;points.resize (scene_cloud_downsampled_-&gt;points.size ());</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    clusters_cloud_-&gt;width = scene_cloud_downsampled_-&gt;width;</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    clusters_cloud_-&gt;height = 1;</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160; </div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; scene_cloud_downsampled_-&gt;points.size (); i++)</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    {</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;      <a class="code" href="structpcl_1_1_point_x_y_z_i.html">pcl::PointXYZI</a> p;</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;      p.getVector3fMap () = scene_cloud_downsampled_-&gt;points[i].getVector3fMap ();</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;      p.intensity = 0.f;</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;      clusters_cloud_-&gt;points[i] = p;</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    }</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160; </div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    <span class="keywordtype">float</span> intens_incr = 100.f / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (clusters.size ());</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    <span class="keywordtype">float</span> intens = intens_incr;</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; clusters.size (); i++)</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    {</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; clusters[i].indices.size (); j++)</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;      {</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        clusters_cloud_-&gt;points[clusters[i].indices[j]].intensity = intens;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;      }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160; </div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;      intens += intens_incr;</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    }</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;  }</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;  <span class="comment">//compute cues</span></div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;  {</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    <a class="code" href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a> tcues (<span class="stringliteral">&quot;Computing cues&quot;</span>);</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    recognition_models_.resize (complete_models_.size ());</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;    <span class="keywordtype">int</span> valid = 0;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (complete_models_.size ()); i++)</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    {</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      <span class="comment">//create recognition model</span></div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;      recognition_models_[valid].reset (<span class="keyword">new</span> RecognitionModel ());</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;      <span class="keywordflow">if</span>(addModel (visible_models_[i], complete_models_[i], recognition_models_[valid])) {</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        indices_[valid] = i;</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        valid++;</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      }</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    }</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    recognition_models_.resize(valid);</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    indices_.resize(valid);</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;  }</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160; </div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;  <span class="comment">//compute the bounding boxes for the models</span></div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  ModelT min_pt_all, max_pt_all;</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;  min_pt_all.x = min_pt_all.y = min_pt_all.z = std::numeric_limits&lt;float&gt;::max ();</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;  max_pt_all.x = max_pt_all.y = max_pt_all.z = (std::numeric_limits&lt;float&gt;::max () - 0.001f) * -1;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recognition_models_.size (); i++)</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;  {</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    ModelT min_pt, max_pt;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <a class="code" href="group__common.html#ga3166f09aafd659f69dc75e63f5e10f81">pcl::getMinMax3D</a> (*complete_models_[indices_[i]], min_pt, max_pt);</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <span class="keywordflow">if</span> (min_pt.x &lt; min_pt_all.x)</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;      min_pt_all.x = min_pt.x;</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    <span class="keywordflow">if</span> (min_pt.y &lt; min_pt_all.y)</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;      min_pt_all.y = min_pt.y;</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160; </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    <span class="keywordflow">if</span> (min_pt.z &lt; min_pt_all.z)</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;      min_pt_all.z = min_pt.z;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; </div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    <span class="keywordflow">if</span> (max_pt.x &gt; max_pt_all.x)</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;      max_pt_all.x = max_pt.x;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160; </div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <span class="keywordflow">if</span> (max_pt.y &gt; max_pt_all.y)</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;      max_pt_all.y = max_pt.y;</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160; </div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;    <span class="keywordflow">if</span> (max_pt.z &gt; max_pt_all.z)</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;      max_pt_all.z = max_pt.z;</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;  }</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;  <span class="keywordtype">int</span> size_x, size_y, size_z;</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;  size_x = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::ceil (std::abs (max_pt_all.x - min_pt_all.x) / res_occupancy_grid_)) + 1;</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;  size_y = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::ceil (std::abs (max_pt_all.y - min_pt_all.y) / res_occupancy_grid_)) + 1;</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;  size_z = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::ceil (std::abs (max_pt_all.z - min_pt_all.z) / res_occupancy_grid_)) + 1;</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;  complete_cloud_occupancy_by_RM_.resize (size_x * size_y * size_z, 0);</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160; </div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recognition_models_.size (); i++)</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;  {</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160; </div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    std::map&lt;int, bool&gt; banned;</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    std::map&lt;int, bool&gt;::iterator banned_it;</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160; </div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; complete_models_[indices_[i]]-&gt;points.size (); j++)</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;    {</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;      <span class="keywordtype">int</span> pos_x, pos_y, pos_z;</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;      pos_x = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::floor ((complete_models_[indices_[i]]-&gt;points[j].x - min_pt_all.x) / res_occupancy_grid_));</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;      pos_y = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::floor ((complete_models_[indices_[i]]-&gt;points[j].y - min_pt_all.y) / res_occupancy_grid_));</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      pos_z = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (std::floor ((complete_models_[indices_[i]]-&gt;points[j].z - min_pt_all.z) / res_occupancy_grid_));</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;      <span class="keywordtype">int</span> idx = pos_z * size_x * size_y + pos_y * size_x + pos_x;</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;      banned_it = banned.find (idx);</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;      <span class="keywordflow">if</span> (banned_it == banned.end ())</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;      {</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;        complete_cloud_occupancy_by_RM_[idx]++;</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;        recognition_models_[i]-&gt;complete_cloud_occupancy_indices_.push_back (idx);</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;        banned[idx] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;      }</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    }</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;  }</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160; </div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;  {</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    <a class="code" href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a> tcues (<span class="stringliteral">&quot;Computing clutter cues&quot;</span>);</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;<span class="preprocessor">#pragma omp parallel for schedule(dynamic, 4) num_threads(omp_get_num_procs())</span></div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; static_cast&lt;int&gt; (recognition_models_.size ()); j++)</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;      computeClutterCue (recognition_models_[j]);</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  }</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160; </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  cc_.clear ();</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;  n_cc_ = 1;</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;  cc_.resize (n_cc_);</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recognition_models_.size (); i++)</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;    cc_[0].push_back (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (i));</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160; </div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;}</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160; </div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::SAOptimize</a>(std::vector&lt;int&gt; &amp; cc_indices, std::vector&lt;bool&gt; &amp; initial_solution)</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;{</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160; </div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  <span class="comment">//temporal copy of recogniton_models_</span></div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  std::vector &lt; boost::shared_ptr&lt;RecognitionModel&gt; &gt; recognition_models_copy;</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;  recognition_models_copy = recognition_models_;</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160; </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;  recognition_models_.clear ();</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160; </div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; cc_indices.size (); j++)</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;  {</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    recognition_models_.push_back (recognition_models_copy[cc_indices[j]]);</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;  }</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160; </div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; recognition_models_.size (); j++)</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;  {</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    boost::shared_ptr &lt; RecognitionModel &gt; recog_model = recognition_models_[j];</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recog_model-&gt;explained_.size (); i++)</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    {</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;      explained_by_RM_[recog_model-&gt;explained_[i]]++;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;      explained_by_RM_distance_weighted[recog_model-&gt;explained_[i]] += recog_model-&gt;explained_distances_[i];</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    }</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160; </div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <span class="keywordflow">if</span> (detect_clutter_)</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    {</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recog_model-&gt;unexplained_in_neighborhood.size (); i++)</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;      {</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        unexplained_by_RM_neighboorhods[recog_model-&gt;unexplained_in_neighborhood[i]] += recog_model-&gt;unexplained_in_neighborhood_weights[i];</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;      }</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    }</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;  }</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160; </div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;  <span class="keywordtype">int</span> occupied_multiple = 0;</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i=0; i &lt; complete_cloud_occupancy_by_RM_.size(); i++) {</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <span class="keywordflow">if</span>(complete_cloud_occupancy_by_RM_[i] &gt; 1) {</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;      occupied_multiple+=complete_cloud_occupancy_by_RM_[i];</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    }</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;  }</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160; </div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;  setPreviousDuplicityCM(occupied_multiple);</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;  <span class="comment">//do optimization</span></div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;  <span class="comment">//Define model SAModel, initial solution is all models activated</span></div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;  <span class="keywordtype">int</span> duplicity;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;  <span class="keywordtype">float</span> good_information_ = getTotalExplainedInformation (explained_by_RM_, explained_by_RM_distance_weighted, &amp;duplicity);</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;  <span class="keywordtype">float</span> bad_information_ = 0;</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;  <span class="keywordtype">float</span> unexplained_in_neighboorhod = getUnexplainedInformationInNeighborhood (unexplained_by_RM_neighboorhods, explained_by_RM_);</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; initial_solution.size (); i++)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;  {</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;    <span class="keywordflow">if</span> (initial_solution[i])</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;      bad_information_ += recognition_models_[i]-&gt;outliers_weight_ * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (recognition_models_[i]-&gt;bad_information_);</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;  }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;  setPreviousExplainedValue (good_information_);</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;  setPreviousDuplicity (duplicity);</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;  setPreviousBadInfo (bad_information_);</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;  setPreviousUnexplainedValue (unexplained_in_neighboorhod);</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160; </div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;  SAModel model;</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;  model.cost_ = <span class="keyword">static_cast&lt;</span>mets::gol_type<span class="keyword">&gt;</span> ((good_information_ - bad_information_</div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;                                               - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (duplicity)</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;                                               - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (occupied_multiple) * w_occupied_multiple_cm_</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;                                               - <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (recognition_models_.size ())</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;                                               - unexplained_in_neighboorhod) * -1.f);</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; </div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;  model.setSolution (initial_solution);</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;  model.setOptimizer (<span class="keyword">this</span>);</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;  SAModel best (model);</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160; </div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;  move_manager neigh (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (cc_indices.size ()));</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160; </div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;  <a class="code" href="classmets_1_1best__ever__solution.html">mets::best_ever_solution</a> best_recorder (best);</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;  <a class="code" href="classmets_1_1noimprove__termination__criteria.html">mets::noimprove_termination_criteria</a> noimprove (max_iterations_);</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;  <a class="code" href="classmets_1_1linear__cooling.html">mets::linear_cooling</a> linear_cooling;</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;  <a class="code" href="classmets_1_1simulated__annealing.html">mets::simulated_annealing&lt;move_manager&gt;</a> sa (model, best_recorder, neigh, noimprove, linear_cooling, initial_temp_, 1e-7, 2);</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;  sa.setApplyAndEvaluate(<span class="keyword">true</span>);</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160; </div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;  {</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;    <a class="code" href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a> t (<span class="stringliteral">&quot;SA search...&quot;</span>);</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;    sa.search ();</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;  }</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160; </div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;  best_seen_ = <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>SAModel&amp;<span class="keyword">&gt;</span> (best_recorder.best_seen ());</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; best_seen_.solution_.size (); i++)</div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;  {</div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    initial_solution[i] = best_seen_.solution_[i];</div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;  }</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160; </div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;  recognition_models_ = recognition_models_copy;</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160; </div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;}</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160; </div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::verify</a>()</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;{</div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;  initialize ();</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160; </div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;  <span class="comment">//for each connected component, find the optimal solution</span></div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> c = 0; c &lt; n_cc_; c++)</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;  {</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;    <span class="comment">//TODO: Check for trivial case...</span></div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;    <span class="comment">//TODO: Check also the number of hypotheses and use exhaustive enumeration if smaller than 10</span></div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    std::vector&lt;bool&gt; subsolution (cc_[c].size (), <span class="keyword">true</span>);</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    SAOptimize (cc_[c], subsolution);</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; subsolution.size (); i++)</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    {</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;      mask_[indices_[cc_[c][i]]] = (subsolution[i]);</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;    }</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;  }</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;}</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160; </div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;<span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::addModel</a>(<span class="keyword">typename</span> pcl::PointCloud&lt;ModelT&gt;::ConstPtr &amp; model,</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    <span class="keyword">typename</span> pcl::PointCloud&lt;ModelT&gt;::ConstPtr &amp; complete_model, boost::shared_ptr&lt;RecognitionModel&gt; &amp; recog_model)</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;{</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;  <span class="comment">//voxelize model cloud</span></div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;  recog_model-&gt;cloud_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;ModelT&gt;</a> ());</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;  recog_model-&gt;complete_cloud_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;ModelT&gt;</a> ());</div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160; </div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;  <span class="keywordtype">float</span> size_model = resolution_;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;  <a class="code" href="classpcl_1_1_voxel_grid.html">pcl::VoxelGrid&lt;ModelT&gt;</a> voxel_grid;</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;  voxel_grid.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (model);</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;  voxel_grid.<a class="code" href="classpcl_1_1_voxel_grid.html#aa5d7831e665977bdce76ed05bd0005cf">setLeafSize</a> (size_model, size_model, size_model);</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;  voxel_grid.<a class="code" href="classpcl_1_1_filter.html#a17115897ca28f6b12950d023958aa641">filter</a> (*(recog_model-&gt;cloud_));</div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160; </div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;  <a class="code" href="classpcl_1_1_voxel_grid.html">pcl::VoxelGrid&lt;ModelT&gt;</a> voxel_grid2;</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;  voxel_grid2.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (complete_model);</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;  voxel_grid2.<a class="code" href="classpcl_1_1_voxel_grid.html#aa5d7831e665977bdce76ed05bd0005cf">setLeafSize</a> (size_model, size_model, size_model);</div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;  voxel_grid2.<a class="code" href="classpcl_1_1_filter.html#a17115897ca28f6b12950d023958aa641">filter</a> (*(recog_model-&gt;complete_cloud_));</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160; </div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;  {</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;    <span class="comment">//check nans...</span></div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    <span class="keywordtype">int</span> j = 0;</div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recog_model-&gt;cloud_-&gt;points.size (); ++i)</div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    {</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;      <span class="keywordflow">if</span> (!pcl_isfinite (recog_model-&gt;cloud_-&gt;points[i].x) || !pcl_isfinite (recog_model-&gt;cloud_-&gt;points[i].y)</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;          || !pcl_isfinite (recog_model-&gt;cloud_-&gt;points[i].z))</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160; </div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;      recog_model-&gt;cloud_-&gt;points[j] = recog_model-&gt;cloud_-&gt;points[i];</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;      j++;</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    }</div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160; </div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    recog_model-&gt;cloud_-&gt;points.resize (j);</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    recog_model-&gt;cloud_-&gt;width = j;</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    recog_model-&gt;cloud_-&gt;height = 1;</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;  }</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160; </div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;  <span class="keywordflow">if</span> (recog_model-&gt;cloud_-&gt;points.size () &lt;= 0)</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;  {</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    PCL_WARN(<span class="stringliteral">&quot;The model cloud has no points..\n&quot;</span>);</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;  }</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160; </div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;  <span class="comment">//compute normals unless given (now do it always...)</span></div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;  <span class="keyword">typename</span> pcl::search::KdTree&lt;ModelT&gt;::Ptr normals_tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;ModelT&gt;</a>);</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation&lt;ModelT, pcl::Normal&gt;</a> NormalEstimator_;</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;  NormalEstimator_ n3d;</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;  recog_model-&gt;normals_.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::Normal&gt;</a> ());</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;  normals_tree-&gt;<a class="code" href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">setInputCloud</a> (recog_model-&gt;cloud_);</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;  n3d.setRadiusSearch (radius_normals_);</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;  n3d.setSearchMethod (normals_tree);</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;  n3d.setInputCloud ((recog_model-&gt;cloud_));</div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;  n3d.compute (*(recog_model-&gt;normals_));</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160; </div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;  <span class="comment">//check nans...</span></div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;  <span class="keywordtype">int</span> j = 0;</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recog_model-&gt;normals_-&gt;points.size (); ++i)</div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;  {</div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    <span class="keywordflow">if</span> (!pcl_isfinite (recog_model-&gt;normals_-&gt;points[i].normal_x) || !pcl_isfinite (recog_model-&gt;normals_-&gt;points[i].normal_y)</div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        || !pcl_isfinite (recog_model-&gt;normals_-&gt;points[i].normal_z))</div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160; </div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    recog_model-&gt;normals_-&gt;points[j] = recog_model-&gt;normals_-&gt;points[i];</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    recog_model-&gt;cloud_-&gt;points[j] = recog_model-&gt;cloud_-&gt;points[i];</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    j++;</div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;  }</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160; </div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;  recog_model-&gt;normals_-&gt;points.resize (j);</div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;  recog_model-&gt;normals_-&gt;width = j;</div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;  recog_model-&gt;normals_-&gt;height = 1;</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160; </div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;  recog_model-&gt;cloud_-&gt;points.resize (j);</div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;  recog_model-&gt;cloud_-&gt;width = j;</div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;  recog_model-&gt;cloud_-&gt;height = 1;</div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160; </div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;  std::vector&lt;int&gt; explained_indices;</div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;  std::vector&lt;float&gt; outliers_weight;</div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;  std::vector&lt;float&gt; explained_indices_distances;</div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;  std::vector&lt;float&gt; unexplained_indices_weights;</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160; </div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;  std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;  std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160; </div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;  std::map&lt;int, boost::shared_ptr&lt;std::vector&lt;std::pair&lt;int, float&gt; &gt; &gt; &gt; model_explains_scene_points; <span class="comment">//which point i from the scene is explained by a points j_k with dist d_k from the model</span></div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;  std::map&lt;int, boost::shared_ptr&lt;std::vector&lt;std::pair&lt;int, float&gt; &gt; &gt; &gt;::iterator it;</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160; </div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;  outliers_weight.resize (recog_model-&gt;cloud_-&gt;points.size ());</div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;  recog_model-&gt;outlier_indices_.resize (recog_model-&gt;cloud_-&gt;points.size ());</div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160; </div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;  <span class="keywordtype">size_t</span> o = 0;</div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; recog_model-&gt;cloud_-&gt;points.size (); i++)</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;  {</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;    <span class="keywordflow">if</span> (!scene_downsampled_tree_-&gt;radiusSearch (recog_model-&gt;cloud_-&gt;points[i], inliers_threshold_, nn_indices, nn_distances, std::numeric_limits&lt;int&gt;::max ()))</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;    {</div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;      <span class="comment">//outlier</span></div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;      outliers_weight[o] = regularizer_;</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;      recog_model-&gt;outlier_indices_[o] = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (i);</div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;      o++;</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;    } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    {</div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; nn_distances.size (); k++)</div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;      {</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;        std::pair&lt;int, float&gt; pair = std::make_pair (i, nn_distances[k]); <span class="comment">//i is a index to a model point and then distance</span></div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;        it = model_explains_scene_points.find (nn_indices[k]);</div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;        <span class="keywordflow">if</span> (it == model_explains_scene_points.end ())</div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;        {</div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;          boost::shared_ptr &lt; std::vector&lt;std::pair&lt;int, float&gt; &gt; &gt; vec (<span class="keyword">new</span> std::vector&lt;std::pair&lt;int, float&gt; &gt; ());</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;          vec-&gt;push_back (pair);</div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;          model_explains_scene_points[nn_indices[k]] = vec;</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;        } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;        {</div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;          it-&gt;second-&gt;push_back (pair);</div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;        }</div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;      }</div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    }</div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;  }</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160; </div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;  outliers_weight.resize (o);</div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;  recog_model-&gt;outlier_indices_.resize (o);</div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160; </div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;  recog_model-&gt;outliers_weight_ = (std::accumulate (outliers_weight.begin (), outliers_weight.end (), 0.f) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (outliers_weight.size ()));</div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;  <span class="keywordflow">if</span> (outliers_weight.size () == 0)</div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;    recog_model-&gt;outliers_weight_ = 1.f;</div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160; </div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;  pcl::IndicesPtr indices_scene (<span class="keyword">new</span> std::vector&lt;int&gt;);</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;  <span class="comment">//go through the map and keep the closest model point in case that several model points explain a scene point</span></div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160; </div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;  <span class="keywordtype">int</span> p = 0;</div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160; </div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;  <span class="keywordflow">for</span> (it = model_explains_scene_points.begin (); it != model_explains_scene_points.end (); it++, p++)</div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;  {</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;    <span class="keywordtype">size_t</span> closest = 0;</div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;    <span class="keywordtype">float</span> min_d = std::numeric_limits&lt;float&gt;::min ();</div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; it-&gt;second-&gt;size (); i++)</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;    {</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;      <span class="keywordflow">if</span> (it-&gt;second-&gt;at (i).second &gt; min_d)</div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;      {</div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;        min_d = it-&gt;second-&gt;at (i).second;</div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;        closest = i;</div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;      }</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    }</div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160; </div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    <span class="keywordtype">float</span> d = it-&gt;second-&gt;at (closest).second;</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;    <span class="keywordtype">float</span> d_weight = -(d * d / (inliers_threshold_)) + 1;</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160; </div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    <span class="comment">//it-&gt;first is index to scene point</span></div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    <span class="comment">//using normals to weight inliers</span></div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;    Eigen::Vector3f scene_p_normal = scene_normals_-&gt;points[it-&gt;first].getNormalVector3fMap ();</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;    Eigen::Vector3f model_p_normal = recog_model-&gt;normals_-&gt;points[it-&gt;second-&gt;at (closest).first].getNormalVector3fMap ();</div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;    <span class="keywordtype">float</span> dotp = scene_p_normal.dot (model_p_normal) * 1.f; <span class="comment">//[-1,1] from antiparallel trough perpendicular to parallel</span></div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160; </div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    <span class="keywordflow">if</span> (dotp &lt; 0.f)</div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;      dotp = 0.f;</div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160; </div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;    explained_indices.push_back (it-&gt;first);</div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;    explained_indices_distances.push_back (d_weight * dotp);</div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160; </div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;  }</div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160; </div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;  recog_model-&gt;bad_information_ = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (recog_model-&gt;outlier_indices_.size ());</div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;  recog_model-&gt;explained_ = explained_indices;</div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;  recog_model-&gt;explained_distances_ = explained_indices_distances;</div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160; </div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;}</div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160; </div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> ModelT, <span class="keyword">typename</span> SceneT&gt;</div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;<span class="keywordtype">void</span> <a class="code" href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification&lt;ModelT, SceneT&gt;::computeClutterCue</a>(boost::shared_ptr&lt;RecognitionModel&gt; &amp; recog_model)</div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;{</div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;  <span class="keywordflow">if</span> (detect_clutter_)</div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;  {</div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160; </div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;    <span class="keywordtype">float</span> rn_sqr = radius_neighborhood_GO_ * radius_neighborhood_GO_;</div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;    std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;    std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160; </div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;    std::vector &lt; std::pair&lt;int, int&gt; &gt; neighborhood_indices; <span class="comment">//first is indices to scene point and second is indices to explained_ scene points</span></div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt; (recog_model-&gt;explained_.size ()); i++)</div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;    {</div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;      <span class="keywordflow">if</span> (scene_downsampled_tree_-&gt;radiusSearch (scene_cloud_downsampled_-&gt;points[recog_model-&gt;explained_[i]], radius_neighborhood_GO_, nn_indices,</div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;          nn_distances, std::numeric_limits&lt;int&gt;::max ()))</div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;      {</div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; nn_distances.size (); k++)</div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;        {</div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;          <span class="keywordflow">if</span> (nn_indices[k] != i)</div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;            neighborhood_indices.push_back (std::make_pair (nn_indices[k], i));</div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;        }</div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;      }</div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;    }</div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160; </div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;    <span class="comment">//sort neighborhood indices by id</span></div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    std::sort (neighborhood_indices.begin (), neighborhood_indices.end (),</div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;        boost::bind (&amp;std::pair&lt;int, int&gt;::first, _1) &lt; boost::bind (&amp;std::pair&lt;int, int&gt;::first, _2));</div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160; </div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;    <span class="comment">//erase duplicated unexplained points</span></div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;    neighborhood_indices.erase (</div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;        std::unique (neighborhood_indices.begin (), neighborhood_indices.end (),</div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;            boost::bind (&amp;std::pair&lt;int, int&gt;::first, _1) == boost::bind (&amp;std::pair&lt;int, int&gt;::first, _2)), neighborhood_indices.end ());</div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160; </div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;    <span class="comment">//sort explained points</span></div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;    std::vector&lt;int&gt; exp_idces (recog_model-&gt;explained_);</div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;    std::sort (exp_idces.begin (), exp_idces.end ());</div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160; </div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;    recog_model-&gt;unexplained_in_neighborhood.resize (neighborhood_indices.size ());</div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;    recog_model-&gt;unexplained_in_neighborhood_weights.resize (neighborhood_indices.size ());</div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160; </div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;    <span class="keywordtype">size_t</span> p = 0;</div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;    <span class="keywordtype">size_t</span> j = 0;</div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; neighborhood_indices.size (); i++)</div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;    {</div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;      <span class="keywordflow">if</span> ((j &lt; exp_idces.size ()) &amp;&amp; (neighborhood_indices[i].first == exp_idces[j]))</div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;      {</div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;        <span class="comment">//this index is explained by the hypothesis so ignore it, advance j</span></div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;        j++;</div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;      } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160;      {</div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;        <span class="comment">//indices_in_nb[i] &lt; exp_idces[j]</span></div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;        <span class="comment">//recog_model-&gt;unexplained_in_neighborhood.push_back(neighborhood_indices[i]);</span></div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;        recog_model-&gt;unexplained_in_neighborhood[p] = neighborhood_indices[i].first;</div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160; </div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;        <span class="keywordflow">if</span> (clusters_cloud_-&gt;points[recog_model-&gt;explained_[neighborhood_indices[i].second]].intensity != 0.f</div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;            &amp;&amp; (clusters_cloud_-&gt;points[recog_model-&gt;explained_[neighborhood_indices[i].second]].intensity</div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;                == clusters_cloud_-&gt;points[neighborhood_indices[i].first].intensity))</div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;        {</div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160; </div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;          recog_model-&gt;unexplained_in_neighborhood_weights[p] = clutter_regularizer_;</div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160; </div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;        } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160;        {</div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;          <span class="comment">//neighborhood_indices[i].first gives the index to the scene point and second to the explained scene point by the model causing this...</span></div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;          <span class="comment">//calculate weight of this clutter point based on the distance of the scene point and the model point causing it</span></div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;          <span class="keywordtype">float</span> d = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (pow (</div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;              (scene_cloud_downsampled_-&gt;points[recog_model-&gt;explained_[neighborhood_indices[i].second]].getVector3fMap ()</div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;                  - scene_cloud_downsampled_-&gt;points[neighborhood_indices[i].first].getVector3fMap ()).norm (), 2));</div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;          <span class="keywordtype">float</span> d_weight = -(d / rn_sqr) + 1; <span class="comment">//points that are close have a strong weight*/</span></div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160; </div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;          <span class="comment">//using normals to weight clutter points</span></div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;          Eigen::Vector3f scene_p_normal = scene_normals_-&gt;points[neighborhood_indices[i].first].getNormalVector3fMap ();</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;          Eigen::Vector3f model_p_normal = scene_normals_-&gt;points[recog_model-&gt;explained_[neighborhood_indices[i].second]].getNormalVector3fMap ();</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;          <span class="keywordtype">float</span> dotp = scene_p_normal.dot (model_p_normal); <span class="comment">//[-1,1] from antiparallel trough perpendicular to parallel</span></div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160; </div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160;          <span class="keywordflow">if</span> (dotp &lt; 0)</div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;            dotp = 0.f;</div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160; </div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;          recog_model-&gt;unexplained_in_neighborhood_weights[p] = d_weight * dotp;</div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;        }</div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;        p++;</div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;      }</div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    }</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160; </div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    recog_model-&gt;unexplained_in_neighborhood_weights.resize (p);</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    recog_model-&gt;unexplained_in_neighborhood.resize (p);</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;  }</div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;}</div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160; </div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_GoHV(T1,T2) template class PCL_EXPORTS pcl::GlobalHypothesesVerification&lt;T1,T2&gt;;</span></div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160; </div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* PCL_RECOGNITION_IMPL_HV_GO_HPP_ */</span><span class="preprocessor"></span></div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160; </div>
<div class="ttc" id="aclassmets_1_1best__ever__solution_html"><div class="ttname"><a href="classmets_1_1best__ever__solution.html">mets::best_ever_solution</a></div><div class="ttdoc">The best ever solution recorder can be used as a simple solution recorder that just records the best ...</div><div class="ttdef"><b>Definition:</b> abstract-search.hh:201</div></div>
<div class="ttc" id="aclassmets_1_1linear__cooling_html"><div class="ttname"><a href="classmets_1_1linear__cooling.html">mets::linear_cooling</a></div><div class="ttdoc">Alternative LCS proposed by Randelman and Grest</div><div class="ttdef"><b>Definition:</b> simulated-annealing.hh:188</div></div>
<div class="ttc" id="aclassmets_1_1noimprove__termination__criteria_html"><div class="ttname"><a href="classmets_1_1noimprove__termination__criteria.html">mets::noimprove_termination_criteria</a></div><div class="ttdoc">Termination criteria based on the number of iterations without an improvement.</div><div class="ttdef"><b>Definition:</b> termination-criteria.hh:132</div></div>
<div class="ttc" id="aclassmets_1_1simulated__annealing_html"><div class="ttname"><a href="classmets_1_1simulated__annealing.html">mets::simulated_annealing</a></div><div class="ttdoc">Search by Simulated Annealing.</div><div class="ttdef"><b>Definition:</b> simulated-annealing.hh:73</div></div>
<div class="ttc" id="aclasspcl_1_1_filter_html_a17115897ca28f6b12950d023958aa641"><div class="ttname"><a href="classpcl_1_1_filter.html#a17115897ca28f6b12950d023958aa641">pcl::Filter::filter</a></div><div class="ttdeci">void filter(PointCloud &amp;output)</div><div class="ttdoc">Calls the filtering method and returns the filtered dataset in output.</div><div class="ttdef"><b>Definition:</b> filter.h:132</div></div>
<div class="ttc" id="aclasspcl_1_1_global_hypotheses_verification_html"><div class="ttname"><a href="classpcl_1_1_global_hypotheses_verification.html">pcl::GlobalHypothesesVerification</a></div><div class="ttdoc">A hypothesis verification method proposed in &quot;A Global Hypotheses Verification Method for 3D Object R...</div><div class="ttdef"><b>Definition:</b> hv_go.h:28</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation</a></div><div class="ttdoc">NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point....</div><div class="ttdef"><b>Definition:</b> normal_3d.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html_ac92dbbea9d923754b3d87d981a6bd131"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">pcl::NormalEstimation::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> normal_3d.h:290</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a82e0be055a617e5e74102ed62712b352"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a82e0be055a617e5e74102ed62712b352">pcl::PointCloud::header</a></div><div class="ttdeci">pcl::PCLHeader header</div><div class="ttdoc">The point cloud header. It contains information about the acquisition time.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:407</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_scope_time_html"><div class="ttname"><a href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a></div><div class="ttdoc">Class to measure the time spent in a scope</div><div class="ttdef"><b>Definition:</b> time.h:118</div></div>
<div class="ttc" id="aclasspcl_1_1_voxel_grid_html"><div class="ttname"><a href="classpcl_1_1_voxel_grid.html">pcl::VoxelGrid</a></div><div class="ttdoc">VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.</div><div class="ttdef"><b>Definition:</b> voxel_grid.h:179</div></div>
<div class="ttc" id="aclasspcl_1_1_voxel_grid_html_aa5d7831e665977bdce76ed05bd0005cf"><div class="ttname"><a href="classpcl_1_1_voxel_grid.html#aa5d7831e665977bdce76ed05bd0005cf">pcl::VoxelGrid::setLeafSize</a></div><div class="ttdeci">void setLeafSize(const Eigen::Vector4f &amp;leaf_size)</div><div class="ttdoc">Set the voxel grid leaf size.</div><div class="ttdef"><b>Definition:</b> voxel_grid.h:223</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt; SceneT &gt;</a></div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_a441f41e648d284d68e1f2015d40f5e7c"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#a441f41e648d284d68e1f2015d40f5e7c">pcl::search::Search::radiusSearch</a></div><div class="ttdeci">virtual int radiusSearch(const PointT &amp;point, double radius, std::vector&lt; int &gt; &amp;k_indices, std::vector&lt; float &gt; &amp;k_sqr_distances, unsigned int max_nn=0) const =0</div><div class="ttdoc">Search for all the nearest neighbors of the query point in a given radius.</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_search_html_ac4a83e895b2a11e89319673117a927fa"><div class="ttname"><a href="classpcl_1_1search_1_1_search.html#ac4a83e895b2a11e89319673117a927fa">pcl::search::Search::getInputCloud</a></div><div class="ttdeci">virtual PointCloudConstPtr getInputCloud() const</div><div class="ttdoc">Get a pointer to the input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> search.h:125</div></div>
<div class="ttc" id="acommon_2include_2pcl_2point__types_8h_html"><div class="ttname"><a href="common_2include_2pcl_2point__types_8h.html">point_types.h</a></div></div>
<div class="ttc" id="acommon_2time_8h_html"><div class="ttname"><a href="common_2time_8h.html">time.h</a></div></div>
<div class="ttc" id="agroup__common_html_ga3166f09aafd659f69dc75e63f5e10f81"><div class="ttname"><a href="group__common.html#ga3166f09aafd659f69dc75e63f5e10f81">pcl::getMinMax3D</a></div><div class="ttdeci">void getMinMax3D(const pcl::PointCloud&lt; PointT &gt; &amp;cloud, PointT &amp;min_pt, PointT &amp;max_pt)</div><div class="ttdoc">Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud</div><div class="ttdef"><b>Definition:</b> common.hpp:228</div></div>
<div class="ttc" id="astructpcl_1_1_point_indices_html"><div class="ttname"><a href="structpcl_1_1_point_indices.html">pcl::PointIndices</a></div><div class="ttdef"><b>Definition:</b> PointIndices.h:13</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_i_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_i.html">pcl::PointXYZI</a></div><div class="ttdef"><b>Definition:</b> point_types.hpp:452</div></div>
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